Apparatus and method for supporting brain function enhancement

ABSTRACT

A training apparatus 1000 using a method of decoding nerve activity includes: a brain activity detecting device 108 for detecting brain activity at a prescribed area within a brain of a subject; and an output device 130 for presenting neurofeedback information (presentation information) to the subject. A processing device 102 decodes a pattern of cranial nerve activity, generates a reward value based on a degree of similarity of the decoded pattern with respect to a target activation pattern obtained in advance for the event as the object of training, and generates presentation information corresponding to the reward value.

TECHNICAL FIELD

The present invention relates to apparatus and method for supportingbrain function enhancement through learning of an event as an object.

BACKGROUND ART

Recently, training systems for training subjects/trainees utilizing thetechnology of computer graphics (CG) such as virtual reality (VR) areavailable. Among such systems, some conduct training while measuringbiological reaction of a subject. A training supporting apparatusdisclosed in Patent Literature 1 detects, as the biological reaction ofthe subject, an active region of his/her brain using near infraredlight, to assist rehabilitation and image training of a subject withdisabilities. Patent Literature 1 is hereby incorporated by reference inits entirety. The training supporting apparatus measures an activeregion or regions of the subject's brain while the subject is working ona calculation problem or a memory task imposed as a training material;after the end of training, the response of the subject in the trainingand the record of measured brain activities are studied together toconfirm the effectiveness of the training. Patent Literature 2 disclosesa training system that always keeps an optimal training scenario inaccordance with the biological reaction of a subject during training.Patent Literature 2 is hereby incorporated by reference in its entirety.Patterns of active parts of the brain measured by fNIR (Functional NearInfrared Spectroscopy), fMRI (Functional Magnetic Resonance Imaging),PET (Positron Emission Tomography) or the like are described as themeans for measuring biological reaction.

Such a technique of scientifically grasping physiological indexes thatwould not otherwise be sensed and feeding these back to enableperception by the subject is referred to as “bio-feedback.”

Though conventional bio-feedback sometimes utilize biologicalinformation such as pulse and breath, it mainly involves human brainwave outputs, converted to visible image or audible sound and output tohumans. Through bio-feedback as such, a subject can grasp the state ofhis/her brain waves on real-time basis. Therefore, bio-feedback ishelpful for the subject to control the state of his/her own brain waves.

By way of example, bio-feedback is utilized for treating abnormalcardiac rhythm, headache, autonomic dysregulation and high bloodpressure and, in addition, it is utilized for mental training in sports.

As a technique of applying bio-feedback to rehabilitation, PatentLiterature 3 discloses a rehabilitation assisting apparatus. Theapparatus is for patients suffering from functional impairment or braindysfunction. The apparatus measures conditions of a rehabilitatingpatient, and evaluates training contents. The apparatus detects thestate of patient's feet using a force sensor or a position sensor,drives thighs, knees and feet joint driving portions to enablecoordinated operations of two lower limbs. The apparatus presents thetiming of motion of the healthy foot or impaired foot as an image, soundor vibration to the subject, and thereby supports effective gaittraining.

Operating a switch or a remote controller of electric appliances, forexample, is not easy for a physically disabled person. As a solution tothis problem, Patent Literature 4 discloses a technique of controllingdevices using human brain potential. Patent Literature 4 is herebyincorporated by reference. According to the technique, a control signalfor controlling a device is output based on the brain wave obtained fromhuman brain potential. According to the technique, the brain wave isconsciously changed using bio-feedback method, and the resulting brainwave is subjected to frequency analysis and arithmetic comparison toobtain the control signal. Another example is disclosed in PatentLiterature 5, which is incorporated herein by reference in its entirety.The apparatus disclosed in this reference includes: a detecting unitdetecting brain potentials at different portions of a human skull andoutputting brain potential data; a pattern generating unit comparingrespective detected brain potentials with a prescribed threshold valueand generating an activity pattern in accordance with the result ofcomparison; a pattern database storing in advance the activity patternsrepresenting the state of activation of the brain and control signalsfor controlling a device in mutually associated manner; and a patternprocessing unit comparing the generated activity pattern with theactivity patterns in the pattern database, extracting a control signalcorresponding to the activity pattern matching the generated activitypattern, and transmitting the control signal to the device. Using thisapparatus, it is possible to control the device by brain potential.

Human sensation and perception are ever-changing in accordance with thesurrounding environment. Most of the changes occur in a certain earlyperiod of human developmental stage, or the period referred to as“critical period.” Adults, however, still keep sufficient degree ofplasticity of sensory and perceptual systems to adapt to significantchanges in surrounding environment. By way of example, Non-PatentLiterature 1 reports that adults subjected to a training using specificperceptual stimulus or exposed to specific perceptual stimulus came tohave improved performance for the training task or improved sensitivityto the perceptual stimulus, and such results of training were maintainedfor a few month to a few years. Non-Patent Literature 1 is herebyincorporated by reference in its entirety. Such a change is referred toas perceptual learning, and it has been confirmed that such changeoccurs in every sensory organ, that is, visual perception, auditoryperception, sense of smell, sense of taste, and tactile perception.

The perceptual learning has various specificities, which are believed tocome from involvement of lower order visual cortex with the perceptuallearning. Further, as reported in Non-Patent Literature 2, there is anunsettled controversy regarding at which stage of visual processing theperceptive learning takes place. Non-Patent Literature 2 is herebyincorporated by reference in its entirety. Thus, it has been unclearwhat method is effective to support perceptual learning.

Bio-feedback used for perceptual learning needs measurement of brainactivity. Various methods therefor have been known, including thefollowing.

-   -   A method of measuring electrocorticogram using electrodes placed        directly on cerebral cortex.    -   Non-invasive method of measuring electroencephalogram (EEG), in        which small currents generated by activities of neurons in the        brain are picked up by electrodes placed on one's skull,        amplified and recorded.    -   Functional MRI (fMRI) visualizing hemodynamic responses related        to human and animal brain activities, utilizing Magnetic        Resonance Imaging (MRI).    -   Magnetoencephalography (MEG), which is an imaging technique of        measuring with high sensitivity magnetic field generated by        electric activities of the brain, using Superconducting Quantum        Interference Device (SQUID).    -   Near-Infrared Spectroscopy (NIRS) measuring increase/decrease of        hemoglobin (Hb) or indexes accompanying oxygen exchange        information, using infrared light in a non-invasive manner        through one's scalp and thereby mapping brain functions.

Conventional studies using fMRI have positively mapped sensorystimulation to humans and brain activities generated in relationtherewith. When nerve activities are considered to be codes, theconventional methods try to find how stimuli are represented by thebrain, or how the nerve activities encode stimuli.

In contrast, reading what stimuli have been applied from the nerveactivities may be called decoding of nerve activities. Decoding of nerveactivities is reported in Non-Patent Literature 3, which is herebyincorporated by reference in its entirety.

Besides, Patent Literatures 6 and 7 and Non-Patent Literatures 4 and 5also report non-invasive measuring methods enabling direct measurementof in-brain nerve activities with high temporal and spatial resolutionsfrom MEG data or EEG data, by utilizing fMRI data. These references arehereby incorporated by reference in their entireties.

Further, Non-Patent Literature 6 reports decoding of brain activitiesusing measurements by electroencephalogram or magnetoencephalography tofind that motion activity to a certain orientation among a plurality oforientations is activated, and utilizing the findings to brain-machineinterface (BMI, hereinafter “interface” will be denoted as “I/F”).Non-Patent Literature 6 is hereby incorporated by reference.

CITATION LIST Patent Literature

-   PTL 1: Japanese Patent Laying-Open No. 2004-294593-   PTL2: Japanese Patent Laying-Open No. 2007-264055-   PTL3: Japanese Patent Laying-Open No. 2005-13442-   PTL4: Japanese Patent Laying-Open No. 2002-125945-   PTL5: Japanese Patent Laying-Open No. 2005-278685-   PTL6: Pamphlet of PCT International Publication 03/057035-   PTL7: Japanese Patent Laying-Open No. 2008-178546

NON PATENT LITERATURE

-   NPL 1: T. Watanabe, J. E. Nanez Sr, S. Koyama, I. Mukai, J.    Liederman and Y. Sasaki: Greater plasticity in lower-level than    higher-level visual motion processing in a passive perceptual    learning task. Nature Neuroscience, 5, 1003-1009, 2002.-   NPL2: Nozomi Ito, Takeo Watanabe, Yuka Sasaki, “Chikaku Gakushu ni    okeru Kinnen no Seika” (Recent developments in Perceptual Learning),    Vision, Vol. 22, No. 2, pp. 115-121, 2010-   NPL3: Miyawaki Y et al. (2009): Visual image reconstruction from    human brain activity using a combination of multiscale local image    decoders. Neuron. December 10; 60(5):915-29.-   NPL4: M. Sato, T. Yoshioka, S. Kajihara, K. Toyama, N. Goda, K.    Doya, and M. Kawato, “Hierarchical Bayesian estimation for MEG    inverse problem,” NeuroImage, vol. 23, pp. 806-826, 2004.-   NPL5: T. Yoshioka, K. Toyama, M. Kawato, O. Yamashita, S.    Nishina, N. Yamagishi, and M. Sato, “Evaluation of hierarchical    Bayesian method through retinotopic brain activities reconstruction    from fMRI and MEG signals,” NeuroImage, vol. 42, pp. 1397-1413,    2008.-   NPL6: Stephan Waldert, Tobias Pistohl, Christoph Braun, Tonio Ball,    Ad Aertsen, Carsten Mehring, “A review on directional information in    neural signals for brain-machine interfaces”, Journal of    Physiology—Paris, 103 (2009) pp. 244-254

SUMMARY OF INVENTION Technical Problem

As described above, however, it has been not necessarily clear how themethods of decoding nerve activities are to be used to enable effectiveperceptual learning, partly because it is unclear at which stage ofbrain visual processing the perceptual learning takes place.

Further, it is not necessarily clear either, how to realize BMI or howto implement rehabilitation utilizing the perceptual learning based onthe methods of decoding nerve activities.

The present invention was made to solve the above-described problems,and its object is to provide apparatus and method for supporting brainfunction enhancement enabling enhancement of prescribed brain functionthrough the user's own action, using a method of decoding nerveactivities.

Solution to Problem

According to an aspect, the present invention provides an apparatus forsupporting brain function enhancement, including: a brain activitydetecting device for detecting a signal indicating a brain activity at aprescribed area within a brain of a subject; a storage device storinginformation of a target activity pattern obtained beforehand withrespect to an event as an object of brain function enhancement; and acontroller. The controller includes a decoding unit for decoding apattern of cranial nerve activity from the signal detected by the brainactivity detecting device, and a computing unit for computing, based ona result of decoding by the decoding unit, in accordance with degree ofsimilarity of the result of decoding to the target activity pattern, areward value corresponding to the degree of similarity. The apparatusfor supporting brain function enhancement further includes an outputdevice for outputting presentation information representing magnitude ofthe reward value to the subject.

Preferably, the computing unit outputs, as the presentation information,information for presenting the presentation information corresponding tothe reward value to the output device, without presenting the event.

Preferably, the event is an object of perception leading to anidentification problem of which class it is classified to in the brain.The decoding unit calculates likelihood of which class the activationpattern of cranial nerve activity corresponds to.

Preferably, the apparatus for supporting brain function enhancementfurther includes: a supporting terminal including the output device; anda processing device including the decoding unit, the storage device andthe computing unit. The supporting terminal includes a communicationunit for transmitting a signal detected by the brain function detectingdevice to the decoding unit.

Preferably, the decoding unit decodes cranial nerve activity of aspecific portion, for example, at the early visual areas, of the brain.

Preferably, the brain activity detecting device includes an fMRI device.

Preferably, the brain activity detecting device includes a device formeasuring EEG and near infrared light from outside of a skull.

According to another aspect, the present invention provides a method ofsupporting brain function enhancement, using a decoding device decodinga pattern of cranial nerve activity pattern from a signal from a brainactivity detecting device for detecting a signal indicating a brainactivity at a specific area within the brain of a subject, including thesteps of: decoding, from the signal detected by the brain functiondetecting device, a cranial nerve activity pattern by means of thedecoding device; calculating, in accordance with a degree of similaritybetween a result of decoding and the target activity pattern obtainedbeforehand for an event as an object of brain function enhancement, areward value corresponding to the degree of similarity; and presenting,to the subject, presentation information indicating magnitude of thereward value.

Preferably, at the step of presenting the presentation information,information for presenting the presentation information corresponding tothe reward value is output to an output device without presenting theevent as the object of brain function enhancement.

Advantageous Effects of Invention

By the apparatus and method for supporting brain function enhancement ofthe present invention, it becomes possible for the subjecthimself/herself to take action to enhance his/her brain function, for anevent as an object of a prescribed brain function, using the method ofdecoding nerve activities in the brain.

Further, by the apparatus and method for supporting brain functionenhancement, it becomes possible to train a subject on an event as anobject of training, using the method of decoding nerve activities in thebrain.

Further, by the apparatus and method for supporting brain functionenhancement, it becomes unnecessary to apply any stimulus correspondingto the event as the object of training to the subject. Therefore, thetraining terminal used by the subject can be made small.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a functional block diagram of a training apparatus 1000 inaccordance with a first embodiment of the present invention.

FIG. 2 is a conceptual diagram showing a relation between visual stimulirelated to orientation and perceptual learning thereof.

FIG. 3 schematically shows a procedure of perceptual learning oftraining apparatus 1000 in accordance with the first embodiment.

FIG. 4 is a flowchart representing the operation of training apparatus1000.

FIG. 5 illustrates a flow of experiment of perceptual learning.

FIG. 6 shows a task imposed on the subjects in pre-test (S200) andpost-test (S206).

FIG. 7 shows sequences of presenting stimuli to the subjects at variousstages of the experiment.

FIG. 8 shows examples of Gabor patches presented as visual stimuli.

FIG. 9 schematically illustrates the process by the decoder.

FIG. 10 is a conceptual diagram illustrating a procedure ofdiscriminating an image viewed by a subject using Multinominal SparseLogistic Regression (hereinafter Sparse Logistic Regression will bereferred to as “SLR”).

FIG. 11 shows a concept of neurofeedback in training apparatus 1000.

FIG. 12 shows orientations as targets, allocated to each subject.

FIG. 13 shows average likelihood of each of three orientations(different by 60 degrees from each other) evaluated by the decoder inthe induction stage.

FIG. 14 shows average likelihood among subjects with respect to thetarget orientation, for the first stage test of day 1 of neurofeedback.

FIG. 15 shows average likelihood for the three orientations calculatedby the decoder from average activity patterns for the entire inductionstage, in areas V1 and V2.

FIG. 16 compares discrimination performance of visual stimuli inpre-test and post-test.

FIG. 17 represents improvement d′ in discrimination sensitivity, as thedifference between the post-test and pre-test.

FIG. 18 shows a relation between the number of days of induction stageand the improvement d′ in discrimination sensitivity.

FIG. 19 plots variation in sensitivity with respect to summation, withthe summation of target orientation likelihood calculated for everytrial of every subject.

FIG. 20 shows results of control experiment by six new subjects.

FIG. 21 is a schematic illustration showing a brain cap as a brainactivity detecting device detecting brain activities.

FIG. 22 is a functional block diagram of a training apparatus 2000 inaccordance with a second embodiment.

DESCRIPTION OF EMBODIMENTS

In the following, configurations of training apparatuses in accordancewith the embodiments of the present invention will be described withreference to the figures. In the embodiments described below, thecomponents and process steps denoted by the same reference charactersare the same or corresponding components or steps and, therefore,description thereof will not be repeated unless necessary.

First Embodiment

Referring to FIG. 1, the training apparatus 1000 in accordance with afirst embodiment of the present invention includes: a brain activitydetecting device 108 for detecting a brain activity in a prescribed areaof a brain of a subject (not shown); a processing device 102 receivingand decoding an output from brain activity detecting device 108 andgenerating information (neurofeedback information) to be presented as afeedback to the subject in accordance with the result of decoding; and adisplay device 130 receiving an output of processing device 102 andpresenting the feedback information to the subject.

As the brain activity-detecting device 108, the fMRI,magnetoencephalography, NIRS, electroencephalogram or a combination ofthese may be used. Of these, fMRI and NIRS detect signals related tocerebral blood flow change and have high spatial resolutions.Magnetoencephalography and electroencephalogram detect change inelectromagnetic field accompanying brain activity and have high temporalresolutions. Therefore, if fMRI and magnetoencephalography are combined,for example, it becomes possible to measure the brain activity with bothspatially and temporally high resolutions. Similarly, if NIRS andelectroencephalogram are combined, a system of measuring brain activitywith high spatial and temporal resolutions can be formed in a small,portable size.

As an output device for presenting the feedback information, here,description will be given assuming a display device 130 used forpresenting visual feedback information to the subject. The feedbackinformation, however, is not limited to visual information, and audioinformation, tactile information or the like may be presented. Theoutput device may be appropriately selected in accordance with the typeof information.

Processing device 102 is not specifically limited and, by way ofexample, it may be realized by a general purpose personal computer. Itgoes without saying that a dedicated hardware may be used.

Processing device 102 includes: an input I/F 110 for receiving a signalfrom brain activity detecting device 108; a computing device 112performing a prescribed computational process on the signal from inputI/F 110 for generating presentation information to be presented to thesubject; a storage device 114 storing a program for enabling anoperation of computing device 112 and information necessary forgenerating the presentation information mentioned above, and attaining afunction of a working memory for the computing device 112; and an outputI/F 124 for outputting a signal for displaying the presentationinformation from computing device 112 to display device 130.

The prescribed computational process executed by computing device 112includes the following:

Decoding the cranial nerve activity pattern from the signals providedthrough input I/F 110; calculating similarity between the decodedactivity pattern and a target activity pattern obtained beforehand withrespect to an event as the object of training; calculating a rewardvalue in accordance with the similarity, based on the calculatedsimilarity; and generating information of target activity pattern,corresponding to the reward value.

Here, the “similarity” may be any of the following:

“Similarity as a pattern” when the pattern of a specific target activitypattern, as a reference obtained beforehand and the pattern of cranialnerve activation at the current time point are output;

Not using the explicit target activity pattern as a reference but usingan evaluation value obtained based on predetermined (one or more)evaluation criteria, a result of determination as to how close thepattern of cranial nerve activation at the current time point is to thetarget; and

Regarding which class the cranial nerve activation at the current timepoint belongs to among a plurality of classes of activity patternsclassified in advance, a value representing degree of possibility (forexample, likelihood) of the current pattern belonging to the targetclass.

The computing device 112 includes a decoding unit 116 operating inaccordance with a program stored in storage device 114, decoding asignal from brain activity detecting device 108, and deriving to whichpattern of activation state of nerve activity receiving what type ofstimulus the present brain activity corresponds; a determining unit 118determining degree of matching of the decoded result decoded by decodingunit 116 with the target activity pattern; a reward calculating unit 120calculating a reward value in accordance with a function that provideslarger value as the degree of matching (similarity) increases, from theresult of determination by determining unit 118; and a presentationinformation generating unit 122 generating presentation informationcorresponding to the calculated reward value, in accordance with apredetermined method.

Here, visual information is presented as the feedback information.Therefore, presentation information generating unit 122 generates imageinformation representing the magnitude of reward value, as thepresentation information. A specific example of the image informationwill be described later.

What is presented by the display device 130 to the subject is not thevisual stimulus itself that causes the pattern of target activation butonly the presentation information corresponding to the reward value.Therefore, even when display device 130 is used as the output device oftraining apparatus 1000, the perception as the object of training is notlimited to visual perception. Beside visual perception, auditoryperception, sense of smell, sense of taste, or tactile perception may bethe object. Further, the information presented by training apparatus1000 to the subject is not limited to image information and it may beauditory information, smell information, taste information or tactileinformation, and it may be any information by which the subject cangrasp the magnitude of reward value.

In the present embodiment, relation between the presentation informationand the magnitude of reward value is selected such that as the magnitudeincreases, the size of a presented disk increases accordingly. Therelation between the magnitude of reward value and the presentationinformation is not limited to such a relation. In an opposite manner,the size of a presented figure may be made smaller as the reward valueincreases. Alternatively, the relation between the two may be selectedsuch that as the reward value increases, the figure reaches a specificsize. In short, what is necessary is that the presentation informationchanges as the reward value changes, in accordance with a specificfunction.

(Perceptual Learning)

In the following, visual stiumuli with respect to orientation, relatedto the stimuli used in the present embodiment and the perceptuallearning thereof will be briefly described.

FIG. 2 is a conceptual diagram showing a relation between visual stimulirelated to orientation and perceptual learning thereof. In thefollowing, the “visual stimulus related to orientation” means presentinga pattern, which is inclined in a specific orientation, to the subject.As a specific example, a Gabor patch of a specific orientation ispresented to the subject.

Here, Gabor patch is one of basic stimulus patterns frequently used invisual science and particularly psychophysical experiments. This isprovided by multiplying sinusoidal grating by two-dimensional Gaussianfunction, and it may be considered to be a part of infinitely continuingsinusoidal grating cutout smooth. Two-dimensional distribution c(x, y)of luminance contrast with the origin being the center will berepresented asc(x,y)=A sin(2πf _(x) x)×exp(−(x ²/2δ² +y ²/2δ²))  (1)(in the case of vertical grating). Here, A represents amplitude, f_(x)represents spatial frequency, and variance δ of Gaussian function isconstant regardless of orientation.

FIG. 2 shows, as examples, three patterns PA, PB and PC of figures ofvertical gratings with different contrasts, to illustrate the stimuluspatterns using Gabor patch. Here, the contrast becomes higher in theorder of PA→PB→PC. As an example of perceptual learning, assume atraining of subjects to be able to discriminate orientation of gratingseven when the contrast of figures is changed.

In general perceptual learning, improved discrimination performancerelated to contrast intensity is observed after perceptual learning(after training) than before the perceptual learning (before training).This can be confirmed by graphic representation of the two performances,as shown in the lower part of FIG. 2. Here, the discriminationperformance can be identified, for example, by gradually adding noise tothe Gabor patch images and determining to which level of noisediscrimination is possible.

FIG. 3 schematically shows a procedure of perceptual learning oftraining apparatus 1000 in accordance with the first embodiment.

As compared with the conventional perceptual learning shown in FIG. 2,training apparatus 1000 of FIG. 1 described above realizes theperceptual learning for the subject in the following manner:

detecting in-brain activity at a prescribed target area of the brain;

decoding signals of detected in-brain activity and thereby obtaining anactivity pattern;

comparing the result of decoding with a target activity pattern (targetpattern);

obtaining, by computation, reward information in accordance with thedegree of matching (similarity) between the two; and

neurofeedbacking the visual information in accordance with the rewardinformation to the subject.

FIG. 4 is a flowchart representing the operation of training apparatus1000.

Referring to FIG. 4, in training apparatus 1000, when the process starts(S100), actions to be trained are recorded for a certain time period(S102), brain activities during such a period of action is detected bybrain activity detecting device 108 (FIG. 1), for example, by fMRI, andbased on the result of detection, the decoder is trained with respect tothe relation between the action and the activity pattern of nerveactivities in the brain, whereby training apparatus 1000 configuresdecoding unit 116 (FIG. 1) (S104). Thereafter, perceptual learning ofthe subject starts.

In-brain activity pattern evoked and induced by the subjecthimself/herself is decoded by decoding unit 116 of training apparatus1000 (S106). Determining unit 118 determines degree of similaritybetween the result of decoding and a target pattern. In accordance withthe result of determination, reward calculating unit 120 calculates thereward value. Presentation information generating unit 122 generatespresentation information corresponding to the reward value, and presentsit to the subject using display device 130, through output I/F 124(S108). The subject continues induction of patterns such that thepresentation information comes to reflect higher reward value. When thetraining level reached a prescribed level (S110), the process ends(S112).

By way of example, a reference for determining whether or not the“training level reached a prescribed level” may be that the reward valuecalculated by reward calculating unit 120 attains to and continuouslykept at a defined level of reward value for a prescribed time period.The end of process may be automatically determined by training apparatus1000, or it may be determined by an administrator of the training.

(Perceptual Learning by Training Apparatus 1000)

In the following, the results of experiments on perceptual learningusing training apparatus 1000 will be described.

In the description below, fMRI is used as an example of a method ofmeasuring in-brain activities of the subject.

Though details will be described later, in short, the results ofexperiment are as follows. The subjects were asked to give efforts tomake the size as large as possible of consecutively presented diskshaped figure of a single color. The disk figure represents thepresentation information generated in accordance with a prescribedfunction related to the reward information of the experiment. The sizeof disk figure was proportional to the likelihood that a temporal fMRIsignal activity pattern of early visual areas (first order visual area(V1 area), second order visual area (V2 area)) of the subject isclassified as the pattern evoked by the presentation of a real andspecific target orientation stimulus. In the present experiment, thesize of disk figure refers to the radius of the disk.

The subjects had no knowledge of what the disk figure represented, norhow, exactly, to control its size.

After this procedure, behavioral performance improved significantly forthe target stimulus, but not for other orientations.

These results indicate that repetitive induction of a targeted neuralactivity pattern in the early visual areas of the adult brain issufficient to cause perceptual learning, without exposure to an externaltarget stimulus, without knowledge of the intention of the experiment.

FIG. 5 illustrates a flow of such an experiment of perceptual learning.

First, for the subjects, a pre-test of behavioral performance isconducted (S200). Here, subjects' discrimination performance for theorientation of Gabor patches was measured, to obtain information relatedto the discrimination performance state before perceptual learning.

Next, subjects were presented with Gabor patches with differentorientations, and decoding unit 116 is trained to decode nerve activitypatterns in the brain observed by fMRI for the pattern of each patch,whereby an fMRI decoder is configured (S202). Though not limiting, it isassumed that decoding unit 116 utilizes a machine learning algorithm,and through learning, it acquires a function of classifying nerveactivity patterns in the brains of subjects to different types ofstimuli presented to the subjects while such activation takes place. Asthe machine learning algorithm, logistic regression, SLR, support vectormachine or the like may be available.

Here, Gabor patches are presented to the subjects as stimuli for decoderconfiguration. In the following, information representing an event asthe object of learning will be more generally referred to as stimulusinformation.

Thereafter, subjects, being monitored by fMRI device, were presentedwith presentation information corresponding to the reward value torealize neurofeedback and thus perceptual learning is done (S204).

Then, subjects' discrimination performance for the Gabor patches wasmeasured, to obtain information related to the discriminationperformance state after perceptual learning (S206).

Details of the flow of FIG. 5 will be described.

The experiment consisted of four stages (S200 to S206) as describedabove, and the time periods for respective stages are as follows.

i) Pre-test (1 day), ii) fMRI decoder construction (1 day), iii)induction (decoded fMRI neurofeedback, 10 days for six subjects, 5 daysfor four subjects), iv) post-test (1 day). Different stages wereseparated by at least 24 hours.

(Pre- and Post-Test Stages)

FIG. 6 shows a task imposed on the subjects in pre-test (S200) andpost-test (S206) in the flow of experiment shown in FIG. 5.

In pre-test and post-test stages, to test whether perceptual learning ofa target orientation occurred as a result of induction of activitypatterns in such early visual areas as V1 and V2, subject' performancein an orientation discrimination task was measured.

As shown in FIG. 6, in each trail, subjects were asked to report whichof three orientations (10°, 70° or 130°) had been presented in a Gaborpatch.

FIG. 7 shows sequences of presenting stimuli to the subjects at variousstages of the experiment.

FIG. 7(a) shows sequences of presenting stimulus in the pre-test andpost-test stages. First, a Gabor patch was presented to the subjects for300 ms, after which the subjects were given 2 seconds to report theGabor orientation they perceived. The presentation and report mentionedabove were repeated for a prescribed number of times.

FIG. 8 shows examples of presented Gabor patches.

First, the orientation of Gabor patch presented to the subjects is oneof 10°, 70° and 130°.

Further, each pattern is presented with different easiness ofdiscrimination, by interposing noise of a plurality of different levelson each pattern.

(fMRI Decoder Configuration Stage)

Next, the fMRI decoder configuration stage (S202) shown in FIG. 5 willbe described.

FIG. 9 schematically illustrates the process by the decoder.

The decoder configuration stage is executed to obtain fMRI activitypatterns from areas V1 and V2 induced by the presentation of each of thethree orientations of Gabor patches to each subject.

As shown in FIG. 7(b), the task imposed on the subjects was to maintainfixation on the Gabor patch. Each task trial of the subjects consistedof a stimulus period (6 sec) and a following, response period (6 sec),and 24 such task trials were executed. At the beginning of each stimulusperiod, the color of the fixation point changed from white to green (inthe figures, represented by dark gray; same in the following), to notifythe subjects of the start of stimulus period. To each Gabor patch, 50%of noise was added. Further, in the stimulus presentation period, aGabor patch of one orientation flashed at 1 Hz. One of the three Gabororientations was randomly assigned to each trial. In 12 trials, that is,half the 24 trials, the spatial frequency of one of the six flashingGabor patches was increased relative to the other 5 having the samespatial frequency. In the remaining 12 trials, the spatial frequency didnot change.

In the response period, no Gabor patch was presented and only thefixation point was presented. In the response period, the subjects wererequired to respond as to whether there was any spatial frequencychange, by pressing a button.

The fMRI signals measured from areas V1 and V2 were converted toactivity amplitude in a voxel virtually set in areas V1 and V2. Adecoder based on multinomial sparse logistic regression was constructedby machine learning algorithm to classify patterns of the measured fMRIsignals to one of the three orientations.

SLR is discussed in Non-Patent Literature 3, Patent Literature 7 and, inaddition, in Brain Communication—Theory and Application—, SAGARAKazuhiko, TANAKA Yasuto, TAKEICHI Hiroshige, YAMASHITA Okito, HASEGAWARyohei, OKABE Tatsuya, MAEDA Taro, Edited by the Institute ofElectronics, Information and Communication Engineers, published byCorona-sha, 1st edition, Apr. 25, 2011, pp. 120-122. This literature ishereby incorporated by reference in its entirety.

Patent Literature 7 mentioned above discloses method and apparatus forpredicting behavior, predicting behavior based on brain activityinformation obtained by such decoding of cranial nerve activities.

In short, SLR is a logistic regression model expanded to Bayesian model,using, as prior distribution of each component of parameter vectors,automatic relevance determination prior distribution, which is a sparseprior distribution. Introduction of sparse prior distribution means thatthe parameter vectors are limited to sparse vectors (only a few elementshave non-zero values and other are 0). SLR prevents over-training bybalancing two criteria, that is, “fitting to learning samples” and“sparse parameter representation.” In addition, in SLR, by obtainingsparse parameter representation, variable selection takes placesimultaneously with parameter learning. Specifically, in the course oflearning, of the dimensions of feature vectors, those regarded asunimportant are removed.

In this experiment, inputs to the decoder are ever-changing state ofbrain activity of the subject, and outputs from the decoder representscalculated likelihood of respective orientations presented to thesubject.

FIG. 10 is a conceptual diagram illustrating a procedure ofreconstructing an image viewed by a subject using Multinominal SLR.

A shown in FIG. 10, when a subject 222 views a specific image 220,activity patterns 224 of areas V1 and V2 in the brain of subject 222 aremeasured as fMRI signals. Based on the measured fMRI signals, contrastprediction 226 is conducted for each small area 228 with multiresolutionin the image. Specifically, the fMRI signals are converted to activationamplitudes in the small area 228 with multiresolution, virtually set inareas V1 and V2. Decoding unit 116 realizes learning of a decoder havingspatial multiresolution using machine learning algorithm based onmultinomial SLR, and by calculating a combination 230 based on linearmodels thereof, computes a re-constructed image 232.

(Induction Stage: Neurofeedback)

Next, the induction stage of step S204 shown in FIG. 5, that is, thestage of neurofeedback will be described.

FIG. 11 shows a concept of neurofeedback in training apparatus 1000.

After configuration of decoding unit 116, the subjects took part in theinduction stage of 5 days or 10 days. In the induction stage, thesubjects learned the method of evoking activity patterns from areas V1and V2 corresponding to the target orientation.

FIG. 7(c) shows a sequence of the induction stage as such.

As shown in FIGS. 7(c) and 11, during each test trial, the subjects wereinstructed to regulate the posterior part of their brains, with the goalof making the size of the solid green disc presented 6 seconds later aslarge as possible (maximum possible size corresponds to the outercircumference of the green disk).

The size of the disc presented in the feedback period corresponded tothe decoder output with respect to the target orientation. The decoderoutput represents the magnitude of likelihood of BOLD (Blood OxygenationLevel Dependent) signal in areas V1 and V2 classified to the targetorientation.

Specifically, the disk size represents how much the pattern obtainedfrom the fMRI signal in the induction period corresponded to the patterninduced by the real targeted Gabor patch orientation presented throughthe above-mentioned fMRI decoder configuration stage (similarity).

The subjects, however, were not informed of what the size represented.The subjects were told that they would receive a payment bonusproportional to the mean size of the feedback disc.

FIG. 12 shows orientations as targets, allocated to each subject.

The subjects, however, were not informed of which orientation is his/hertarget orientation.

Note that all other information, including the target orientation, thepurpose of the neurofeedback, and the meaning of the disc size, waswithheld from the subject. (Activation Patterns of In-Brain ActivitiesLearned by the Subjects in the Induction Stage)

Actual presentation of the target orientation evokes an activity patternin the neurons of areas V1 and V2 of the subject's brain. In thefollowing, whether the subjects could learn to induce an activitypattern corresponding to the activity pattern by him/her without anypresentation of the target orientation will be examined.

To test whether subjects could induce the neural activity patterns,during the induction stage, first, the following test was conducted.First, for each subject, a target orientation and two other orientationsrotated by ±60° from the target orientation were determined. We examinedwhether outputs of the decoder could be biased toward the selectedtarget orientation by the subject, compared with the other twoorientations.

FIG. 13 shows average likelihood of each of three orientations(different by 60 degrees from each other) evaluated by the decoder inthe induction stage.

The overall mean likelihood of the target orientation in the decoderoutput for areas V1 and V2 was significantly higher than chance acrossthe subjects on average during the induction stage (result oft-test:t(9)=3.34, P<10⁻²).

These results indicate that the subjects could induce activity patterns(in areas V1 and V2) that closely corresponded to the activity patternevoked by the target orientation and are distinguishable from theactivity patterns evoked by the other orientations in areas V1 and V2.

FIG. 14 shows average likelihood among subjects with respect to thetarget orientation, for the first stage test of day 1 of neurofeedback.

As can be seen from FIG. 14, the mean likelihood across the subjects ofthe target orientation for the first 30 trials was around chance level.

From the comparison between FIGS. 13 and 14, it can be seen that therewas no significant orientation bias for the target orientation beforeneurofeedback, and that subjects quickly learned to induce activitypatterns matching the target orientation even during the firstneurofeedback day.

FIG. 15 shows average likelihood for the three orientations calculatedby the decoder from average activity patterns for the entire inductionstage, in areas V1 and V2.

In FIG. 15, to further confirm that subjects could induce the neuralactivity patterns, the same decoder was applied to the overall meanactivation pattern, rather than trial-by-trial activation patterns, inareas V1 and V2 during the induction stage for each subject.

Consistent with the results of trial-by-trial decoding, the meanlikelihood of the target orientation was significantly higher thanchance (result of t-test:t(9)=2.69, P=0.02).

This result further supports that, during the induction stage, subjectscould learn to consistently induce a neural activity pattern in areas V1and V2 that corresponded to the activity pattern evoked by thepresentation of the target orientation.

(Were the Subjects Aware of the Purpose of the Induction Stage?)

After the post-test stage, subjects were asked about what they thoughtthe size of the feedback disc represented. None of their responses waseven remotely related to the true workings of the experiment.

Then, after being told that the disc size represented the possibility ofone of three orientations, subjects were asked to report the orientationthey thought they had been trained on. Only 3 out of 10 subjectscorrectly chose her/his target orientation. The percentage of thechoices of the target orientation was statistically undistinguishablefrom what would be expected from chance (Chi-square test, x²=0.20,P=0.90). These results suggest that the subjects were aware neither ofthe purpose of the induction stage nor the orientation corresponding tothe induced pattern of neural activity.

The purpose of the induction stage was to have subjects learn and thencontinue to induce activity patterns that corresponded to the neuralactivity pattern in V1 and V2 evoked by the presentation of a targetorientation at the decoder construction stage.

As previously mentioned, the results indicate that on the first day ofthe induction stage, the subjects already learned to induce activitypatterns that were classified as the target orientation more frequentlythan as the other two orientations. Further, this tendency becamestronger as neurofeedback trials progressed.

(Improvement of Discrimination Performance Through Perceptual Learning)

FIG. 16 compares discrimination performance of visual stimuli inpre-test and post-test.

Three-way (test stage×orientation×S/N ratio) analysis of variance withrepeated measures indicated significant main effect of S/N ratio (F(3,27)=683.17, P<10⁴) and significant effect of interaction between teststage, orientation, and S/N ratio (F(6, 54)=2.68, P=0.02).

FIG. 17 represents improvement d′ in discrimination sensitivity, as thedifference between the post-test and pre-test.

Post-hoc t-test between accuracies in pre- and post-tests revealed thatdiscrimination performance for the target orientation significantlyimproved at the 6% S/N ratio (t(9)=5.76, P<10⁻² with Bonferronicorrection by 12 comparisons).

Improvement in discrimination sensitivity d′ in the pre-test subtractedfrom that of the post-test was significantly greater than zero for thetarget orientation at the 6% S/N ratio (t(9)=5.60, P<10⁻³ withBonferroni correction by 3 comparisons.

From these results, we conclude that mere repetitive induction of theactivity patterns corresponding to activity patterns in areas V1 and V2evoked by the presentation of a target orientation caused the activitypatterns in the subjects, without presenting the pattern. In otherwords, the subjects has accomplished perceptual learning specific to theorientation.

(Relationship Between the Likelihood of the Target Orientation in V1 andV2 and Sensitivity (d′) Changes)

FIG. 18 shows a relation between the number of days of induction stageand the improvement d′ in discrimination sensitivity.

The sensitivity changes for the subjects with 10 days training(induction) were larger than that with 5 days training. The results wereconsistent with the general tendency that the magnitude of perceptuallearning is larger with longer training until it reaches an asymptote.

FIG. 19 plots variation in sensitivity with respect to summation, withthe summation of target orientation likelihood calculated for everytrial of every subject.

The correlation was even stronger for the likelihood summation (r=0.87,P=10⁻³) than the average likelihoods (r=0.74, P=0.01).

These results indicate that the closer the pattern that the fMRIneurofeedback induced was to that of the pattern evoked by the actualpresentation of the target orientation and the longer the training was,the larger the magnitude of performance improvement after training.

FIG. 20 shows results of control experiment by six new subjects.

To test whether the perceptual learning observed in the main experimentresulted simply from subjects' participation in the test stages, weconducted a control experiment with 6 new subjects. For these subjects,only the pre- and post-test stages were conducted without the inductionstage. The time interval between the pre- and post-tests was identicalto the mean time interval in the main experiment.

As shown in FIG. 20, no significant performance improvement wasobserved, indicating that the perceptual learning in the main experimentwas not due merely to the effects of the test stages.

Though the subjects were asked to discriminate three orientations in thedescription above, naturally, the number is not limited to three, andlarger number of orientations may be used. The visual information as theobject of discrimination is not limited to orientation as describedabove. Other visual stimulus to which neurons in the early visual areasrespond, such as spatial frequency or color may be available. The objectto be discriminated by the subjects is not necessarily limited to visualinformation, and more generally, it may be any object “that can lead tothe identification problem of to which class it is classified.”

As described above, by the training apparatus in accordance with thepresent embodiment, perceptual training using the method of decodingnerve activity can be realized without presenting specific stimulusinformation to the subjects.

As described above, it is confirmed that the perceptual learning itselfoccurs in every sensory organ, that is, visual perception, auditoryperception, sense of smell, sense of taste, and tactile perception.Therefore, applications of training apparatus 100 may include thefollowing.

i) Relaxation Training

Relaxation training refers to training for decreasing sensitivity totension or to feeling of anxiety, so as to alleviate hyper tension orcompetition anxiety one suffers from immediately before the start ofsport competition. In-brain activity of the competitor in mentallyrelaxed state and the state of in-brain activity during training arecompared by training apparatus 1000, and the degree of matching with therelaxed state is fed back as the feedback information to the competitor,whereby relaxation training of the competitor by training apparatus 1000can be executed.

The usage of such relaxation is not limited to sports training.Generally, in normal life, training using training apparatus 1000 may beconducted to realize deeper relaxation of the user at the time for rest.

ii) Image Training

Image training refers to a method of alleviating stage fright during acompetition and keeping concentration, in which a competitor imagesvisual images and muscle movements as real as possible assuming anexercise or a game scene, and he/she simulates the flow of the game orthe atmosphere of the venue beforehand.

Image training in this sense has already been utilized by actualathletes as mental training to realize one's potential in a relaxedstate on a real stage.

Here, provided that “the state of brain activity in which the subject isunder tension for the competition in the real part but simultaneouslynot too serious” has been acquired as data beforehand, it is possible toexecute the mental training on the subject using training apparatus1000, with the degree of matching between the state of in-brain activityas such and the in-brain activity during the training fed back as thefeedback information.

Image training has been used for subsidiary practice to master motorskills. By way of example, the state of in-brain activity when a subjecthits a beautiful shot on a golf course may be obtained as databeforehand; the degree of matching with the in-brain activity of thesubject during training may be measured by training apparatus 1000; andthe results may be fed back to the subject as the feedback information.In this manner, mental training for mastering motor skills can beexecuted using training apparatus 1000.

iii) Treatment of Disease Resulting from Brain Function

When a part of brain function of a patient suffering, for example, frommood disorder such as depression or dementia tends to decline,currently, it is possible to suppress the symptom or to slow theprogression of the disease to some extent by medication. If data of thestate of in-brain activity when the patient is in a desirable conditioncould be obtained beforehand, training of the patient using trainingapparatus 1000 would be effective to improve the condition of thepatient.

It is known that, when a part of the brain is damaged, for example, byan injury, other part of the brain possibly acts to compensate for thedamaged portion. By measuring the in-brain activity of the subject insuch compensation stage and feeding back the results, training apparatus1000 may possibly be used as a method of rehabilitation.

iv) Training of Sense of Smell, Sense of Taste and Tactile Perception

Generally, it is difficult to artificially create stimulus for trainingsense of smell, sense of taste and tactile perception. Trainingapparatus 1000 after configuration, however, does not require artificialcreation of such stimulus. Therefore, training on the subject ispossible with respect to these sensory organs.

v) Improvement of Memory Retention

It is reported that external stimulus of a certain frequency duringnon-REM sleep is effective for memory consolidation, in Lisa Marshall,Halla Helgadottir, Matthias Molle, Jan Born, “Boosting slow oscillationsduring sleep potentiates memory”, Nature, Vol. 444, 30 Nov. 2006, pp.610-613.

According to this report, external stimulus has an influence on theactivation of a portion of the brain related to memory consolidation. Inother words, if the state of activity of that portion which is activatedwere obtained as data and any reward could somehow be given to thesleeping subject, training apparatus 1000 could be used as an apparatusassisting memory consolidation during sleep. As the reward in such asituation, by way of example, if the subject is in a desirable state,fine fragrance may be given to the subject and if the subject is in anundesirable state, weak mechanical or electric unpleasant stimulus maybe given to the subject.

In training apparatus 1000, what is presented by display device 130 tothe subject (trainee) is not the stimulus information itself thatgenerates the pattern of target activation. What is presented isabsolutely the presentation information corresponding to the rewardvalue. It is unnecessary for the subject to be aware of the event itselfas the object of learning. Therefore, even if the event as the object oflearning is what the subject despises or what he/she wants to stay awaybefore learning, learning of such an object is still possible. By way ofexample, assume that one has a “phobia (phobic disorder)” that producesintense fear of a specific thing as to cause inconvenience in daily lifeor social activity. Training apparatus 1000 can be used for training toalleviate such symptom.

As described above, training apparatus 1000 is capable of “training” ofbrain function and, more generally, capable of supporting enhancement ofbrain function. In this sense, the apparatus that can realize proceduresas described above will be referred to as an “apparatus for supportingbrain function enhancement.”

Second Embodiment

In the following, a configuration of the training apparatus inaccordance with the second embodiment will be described.

FIG. 21 is a schematic illustration showing a brain cap detectingsignals indicating a brain activity at a prescribed area in the brain ofthe subject.

Brain cap 10 includes a cap-shaped holder 10A covering one's skull, anda plurality of (for example, several to several hundreds of) firstsensors 11 and second sensors 12 provided on an outer circumferentialsurface of holder 10A. In FIG. 21, for simplicity of description,sensors are schematically shown spaced by an individual space from eachother. Actually, the first and second sensors 11 and 12 are arrangedrelatively dense at an equal pitch (for example, at an interval of aboutfew millimeters).

If the signals representing brain activity of the subject are to bedetected from a limited area, the first and second sensors may beprovided only on a specific area of holder 10A of brain cap 10.

First sensor 11 is, for example, an electroencephalogram (EEG) sensorfor measuring electric activity generated by brain activity in anon-invasive manner. Each first sensor 11 serves as anelectroencephalogram (EEG) and each sensor 11 measures and outputs as anelectric signal time-change in brain magnetic field accompanying brainactivity at the arranged position. The first sensor 11 has high temporalresolution and capable of measurement in millisecond order.

The second sensor 12 is, for example, a near-infrared sensor NIRS. Eachsecond sensor has a light emitting element emitting near infrared lightof relatively short wavelength, and a light receiving element receivingthe reflected infrared light. Each second sensor 12 detects amount ofabsorption of the light emitted from the light emitting element inaccordance with the intensity of light received by the light receivingelement, and from the output signal of the light receiving element,measures the state of brain blood flow in a non-invasive manner. Unlikeelectric field or magnetic field, the second sensor 12 is free from theinfluence of other areas and, therefore, it has superior spatialresolution and it is capable of measurement in the order of a fewmillimeters or tens of millimeters.

The first and second sensors 11 and 12 as such enable monitoring of thebrain activity with small size. Therefore, these sensors can be mountedeasily on brain cap 10 such as described above. Measurement of in-brainactivity patterns of a subject does not require any large apparatus.

In the present embodiment, utilizing the brain cap 10 shown in FIG. 21,brain activities of a subject are time-sequentially observed, and basedon the observational data, brain activities of the subject arepredicted.

FIG. 22 is a functional block diagram of a training apparatus 2000 inaccordance with the second embodiment.

Training apparatus 2000 in accordance with the second embodiment differsfrom training apparatus 1000 in accordance with the first embodiment inthe following points.

First difference is that brain cap 100 such as described above is usedas the detector for detecting brain activities.

The second difference is that in place of processing device 102 shown inFIG. 1, the apparatus includes a training terminal 106 connected tobrain cap 100, and a processing device 302 capable of wirelesscommunication with training terminal 106, for performing a prescribedprocessing on measurement signals received from the training terminaland thereby calculating presentation information and transmitting it inwireless manner to training terminal 106.

Training terminal 106 includes: a display device 130 similar to the oneused in the first embodiment; a computing unit 128 converting themeasurement signals from brain cap 100 to a prescribed transmissionformat; and a communication unit 126 transmitting the signals convertedto the transmission format to processing device 302 and receiving thepresentation information as the feedback information from processingdevice 302. Computing unit 128 further generates visual informationbased on the presentation information received by communication unit 126and presents it to the subject through display device 130.

Processing device 302 includes, in place of input I/F 110 and output I/F124 shown in FIG. 1, an input/output I/F 111 for wireless communicationto and from training terminal 106, a computing device 312 having asimilar configuration as computing device 112 of FIG. 1, and a storagedevice 114 storing a program executed by computing device 312.

The present embodiment is characterized in that training terminal 106and processing device 302 are separate bodies, and hence, brain cap 100can be used at a location far from the processing device 302. Therefore,the communication method between training terminal 106 and processingdevice 302 is not limited to wireless communication simply connectingthe two devices directly. By way of example, communication through anetwork may be available. Direct cable connection between the two isalso possible.

In the present embodiment, the presentation information corresponding tothe reward value is generated by the side of processing device 302. Thepresent invention, however, is not limited to such an embodiment.Processing device 302 may calculate the reward value, and on the side oftraining terminal 106, computing unit 128 may receive the reward valueand generate the presentation information by a prescribed computation.

Similar to computing device 112 of FIG. 1, computing device 312 includesdecoding unit 116, determining unit 118, reward calculating unit 120 andpresentation information generating unit 122. Different from computingdevice 112 of FIG. 1, however, it further includes a pre-processing unit113 receiving and processing the measurement signals transmitted fromtraining terminal 106 through input/output I/F 111 and generatingsignals of a format decodable by decoding unit 116.

Specifically, in training apparatus 2000 in accordance with the secondembodiment, the functions attained by brain activity detecting device108 for detecting brain activities at a prescribed area within the brainin the first embodiment are realized by brain cap 100, training terminal106 and pre-processing unit 113.

Except for these points, the configuration is the same as that oftraining apparatus 1000 in accordance with the first embodiment and,therefore, description thereof will not be repeated.

By the configuration as described above, training apparatus 2000 inaccordance with the second embodiment attains, in addition to theeffects attained by training apparatus 1000 in accordance with the firstembodiment, the effect that the activities of the subject are notlimited by the location of training apparatus 2000, since the subjectcan be trained wearing brain cap 100 and holding training terminal 106smaller than processing device 102. Further, display device 130 can bereduced in size, since it has only to display the feedback information.It is noted that training apparatus 2000 in accordance with the secondembodiment can also be used as the apparatus for supporting brainfunction enhancement, as does the training apparatus 1000 in accordancewith the first embodiment.

The embodiments as have been described here are mere examples and shouldnot be interpreted as restrictive. The scope of the present invention isdetermined by each of the claims with appropriate consideration of thewritten description of the embodiments and embraces modifications withinthe meaning of, and equivalent to, the languages in the claims.

INDUSTRIAL APPLICABILITY

The apparatus for supporting brain function enhancement in accordancewith the present invention can be applied to perceptual learning,rehabilitation, sports relaxation, and adaptive learning with respect toenvironment.

REFERENCE SIGNS LIST

102, 302 processing device; 106 training terminal; 108 brain activity 2detecting device; 110 input I/F; 111 input/output I/F; 112, 312computing device; 113 pre-processing unit; 114 storage device; 116decoding unit; 118 determining unit; 120 reward calculating unit; 122presentation information generating unit; 124 output I/F; 128 computingunit; 130 display device; 1000, 2000 training apparatus.

The invention claimed is:
 1. An apparatus for supporting brain functionenhancement of a prescribed brain function by enabling effectivelearning, comprising: a brain activity detecting device for detecting asignal indicating brain activity at a prescribed area within a brain ofa subject; a storage device storing information of a target classcorresponding to a target activity pattern obtained beforehand withrespect to brain function enhancement; and processing circuitryconfigured to, during a brain activity decoder configuration stage,perform the following steps present, for a predefined time period, thesubject with a plurality of stimulus events, where a stimulus event is aperceptual stimulus which activates portions of the subject's brain;receive brain activity signals from the brain activity detecting devicewhile the subject is exposed to the plurality of stimulus events, eachof the plurality of stimulus events corresponding to one of a pluralityof classes including the target class; and train, using a machinelearning algorithm, the processing circuitry to decode cranial nerveactivity patterns from brain activity signals and classify the cranialnerve activity patterns to one of the plurality of classes of activitypatterns classified in advance; the processing circuitry furtherconfigured to, during a neurofeedback stage for training the subject,repeatedly perform the following steps until a predetermined conditionis satisfied, exposing the subject to a reward stimulus so that thesubject tries to increase the reward while maintaining the subject inthe absence of the awareness of the relation between the reward stimulusand the target class; receiving a brain activity signal inducted by thesubject from the brain activity detecting device while the subject isexposed to the reward stimulus; deriving, from the received brainactivity signal, a current cranial nerve activity pattern; calculating avalue representing a degree of possibility of said current cranial nerveactivity pattern belonging to the target class, computing, based on saidcalculated value, in accordance with degree of similarity of saidcurrent cranial nerve activity pattern to said target class, a rewardvalue corresponding to said degree of similarity; and altering thereward stimulus to represent a magnitude of said reward value withoutrevealing to the subject the stimulus event corresponding to the targetclass; and outputting said altered reward stimulus to said subject,wherein said stimulus event corresponding to the target class is anobject of perception leading to an identification problem of which classit is classified to in the brain; and said processing circuitry isfurther configured to calculate likelihood of which class saidactivation pattern of cranial nerve activity corresponds to.
 2. Theapparatus for supporting brain function enhancement according to claim1, wherein the prescribed area within the brain of the subjectcorresponds to specific portions of the brain.
 3. The apparatus forsupporting brain function enhancement according to claim 1, wherein saidbrain activity detecting device includes a functional Magnetic ResonanceImaging device.
 4. The apparatus for supporting brain functionenhancement according to claim 1, wherein said brain activity detectingdevice includes a device for measuring electroencephalogram and nearinfrared light from outside of a skull.
 5. A neurofeedback method ofsupporting brain function enhancement, using a brain activity detectingdevice for detecting a signal indicating a brain activity at a specificarea within the brain of a subject and processing circuitry trained todecode cranial nerve activity patterns from brain activity signals andclassify the cranial nerve activity pattern to one of a plurality ofclasses of activity patterns classified in advance respectivelycorresponding to stimulus events in association with a possibility, saidmethod comprising the steps of: training, during a brain activitydecoder configuration stage, said processing circuitry through machinelearning such that said plurality of classes includes a target classcorresponding to a target activity pattern obtained with respect tobrain function enhancement; exposing, during a neurofeedback stage fortraining the subject, the subject to a reward stimulus and asking thesubject to increase the reward without the subject having any knowledgeof how to alter it; receiving, during the neurofeedback stage, a brainactivity signal induced by the subject from the brain activity detectingdevice while the subject is exposed to the reward stimulus; deriving,from the received brain activity signal, a current cranial nerveactivity pattern; classifying, using the trained processing circuitry,said current cranial nerve activity pattern to one of the plurality ofclasses respectively corresponding to stimulus events presented to thesubject prior to said neurofeedback stage; calculating, based on thecurrent cranial nerve activity pattern, a value representing a degree ofpossibility of said current cranial nerve activity pattern belonging tosaid target class; calculating, in accordance with a degree ofsimilarity of said current cranial nerves activity pattern to saidtarget class, a reward value corresponding to said degree of similaritybased on said calculated value; altering the reward stimulus the subjectis exposed to during said neurofeedback stage to represent a magnitudeof said reward value, without revealing to the subject the stimulusevent corresponding to the target class; and during said neurofeedbackstage, presenting the altered reward stimulus to the subject withoutrevealing to the subject the stimulus event corresponding to the targetclass, wherein said stimulus event corresponding to the target class isan object of perception leading to an identification problem of whichclass it is classified to in the brain; and said processing circuitry isfurther configured to calculate likelihood of which class saidactivation pattern of cranial nerve activity corresponds to.
 6. Theapparatus for supporting brain function enhancement according to claim1, wherein said processing circuitry is trained based on a sparselogistic regression; and said degree of possibility is calculated usingthe sparse logistic regression, said degree of possibility being thelikelihood of said current cranial nerve activity pattern belonging tothe target class.
 7. The neurofeedback method for supporting brainfunction enhancement according to claim 5, wherein said processingcircuitry is trained based on a sparse logistic regression; and saiddegree of possibility is calculated using the sparse logisticregression, said degree of possibility being the likelihood of saidcurrent cranial nerve activity pattern belonging to the target class.